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Randomized local-spin mutual exclusion
Randomized local-spin mutual exclusion

Chapter 2: Fundamentals of the Analysis of Algorithm Efficiency
Chapter 2: Fundamentals of the Analysis of Algorithm Efficiency

Slide Set 2
Slide Set 2

... Memory acts like a medium; Prevent loss of data while waiting ...
Document
Document

Document
Document

Implementation of Multiple Constant Multiplication
Implementation of Multiple Constant Multiplication

... embedded system designs. Many embedded systems use DSP algorithms for image processing and video processing which are very compute intensive. A custom hardware implementation of these algorithms can provide a way by which the requirements for time and energy of the embedded systems can be met. The c ...
Conservation scores
Conservation scores

... • Estimates mean number of substitutions in each aligned genome to estimate neutral evolution rate • Original score is “rejected substitutions”: the number of substitutions expected under ‘neutrality’ minus the number of substitutions observed at each aligned position • New scores based on ML fit of ...
Database homology searching
Database homology searching

... and in the interests of completeness there is: • tblastx: searches a DNA sequence (translated in all six reading frames) against a DNA database (translated in all six reading frames). finally • Psi-blast an iterative process using position specific subst matrix PSSM to find distantly related sequenc ...
U.C. Berkeley — CS270: Algorithms Lectures 13, 14 Scribe: Anupam
U.C. Berkeley — CS270: Algorithms Lectures 13, 14 Scribe: Anupam

... The streaming model is one way to model the problem of analyzing massive data. The model assumes that the data is presented as a stream (x1 , x2 , · · · , xm ), where the items xi are drawn drawn from a universe of size n. Realtime data like server logs, user clicks and search queries are modeled by ...
Towards the development of standardized methods for comparison
Towards the development of standardized methods for comparison

... of structure comparison techniques, it is advisable to use several programs in parallel and to choose the optimal alignment in the way reported here. Availability and implementation: The computer software that implement the method described here is freely available at http://melolab. org/stovca. Con ...


... Consensus length 19, 10 matches(|), 6 mismatches (x), 1 single gap (-, SG), 1 extended gap (--, EG) Score: matches – (SG+EG)*P – (EG-1)*PE = (P: penalty for SG; PE: penalty for EG) ...
Sequences and Series—A Recap
Sequences and Series—A Recap

... Another common sequence is called geometric. It is like an arithmetic sequence, except that instead of adding a fixed number each time, you multiply by a fixed number. Thus the sequence begins a, ar, ar2, ar3, etc. Note that the first term has no r’s in it, the second has one, the third has two, and ...
Promoter Analysis for Intestinally
Promoter Analysis for Intestinally

... c. Provide evidence, if possible, for the ELT-2 theory of intestinal gene regulation 2. Summary: a. Motif Discovery is complete for all 74 C. elegans genes, 57 C. briggsae orthologues and 38 C. remanei orthologues b. Hit sequences have been extracted and aligned c. We need to discuss how to generate ...
Blast intro slides ppt
Blast intro slides ppt

BLAST intro slides ppt
BLAST intro slides ppt

The Unbearable Lightness of Consensus
The Unbearable Lightness of Consensus

Bio101 Development Guide.pages
Bio101 Development Guide.pages

Chapter 8: Dynamic Programming
Chapter 8: Dynamic Programming

... Example 3: Path counting Consider the problem of A counting the number of shortest paths from point A to point B in a city with perfectly horizontal streets and vertical avenues ...
Chapter 8: Dynamic Programming
Chapter 8: Dynamic Programming

Deployment of Sensing Devices on Critical Infrastructure
Deployment of Sensing Devices on Critical Infrastructure

... Is the algorithm or data structure naturally suited to recursion? A list, such as data read from the keyboard, is not naturally recursive structure. Moreover, the algorithm is not a logarithmic algorithm. ...
Department of Health Information Management
Department of Health Information Management

... comparison algorithm optimized for speed used to search sequence databases for optimal local alignments to a query • It is the most widely used and referenced computational biology resource • The central idea of the BLAST algorithm is to confine attention to segment pairs that contain a word pair of ...
BLAST_tutorial
BLAST_tutorial

... Use BLASTN for simple nucleotide against nucleotide searches Use BLASTP for simple protein versus protein searches Use TBLASTN for a protein query versus a DNA library search Use BLASTX for DNA query versus protein library Use TBLASTX for a translated DNA query against a translated DNA database ...
Motifs and motif prediction methods I - BIDD
Motifs and motif prediction methods I - BIDD

... •Inclusion of all possible information to maximise overall signal of protein/domain i. e., a full representation of features in the aligned sequences •Able to detect distant relationships with only few well conserved residues ...
GNoC - Technion - Electrical Engineering
GNoC - Technion - Electrical Engineering

Comparison of Genomes using High-Performance - FACOM
Comparison of Genomes using High-Performance - FACOM

< 1 ... 7 8 9 10 11 12 13 14 15 ... 21 >

Smith–Waterman algorithm

The Smith–Waterman algorithm performs local sequence alignment; that is, for determining similar regions between two strings or nucleotide or protein sequences. Instead of looking at the total sequence, the Smith–Waterman algorithm compares segments of all possible lengths and optimizes the similarity measure.The algorithm was first proposed by Temple F. Smith and Michael S. Waterman in 1981. Like the Needleman–Wunsch algorithm, of which it is a variation, Smith–Waterman is a dynamic programming algorithm. As such, it has the desirable property that it is guaranteed to find the optimal local alignment with respect to the scoring system being used (which includes the substitution matrix and the gap-scoring scheme). The main difference to the Needleman–Wunsch algorithm is that negative scoring matrix cells are set to zero, which renders the (thus positively scoring) local alignments visible. Backtracking starts at the highest scoring matrix cell and proceeds until a cell with score zero is encountered, yielding the highest scoring local alignment. One does not actually implement the algorithm as described because improved alternatives are now available that have better scaling (Gotoh, 1982) and are more accurate (Altschul and Erickson, 1986).
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